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During software evolution technical debt (TD) follows a constant ebb and flow, being incurred and paid back, sometimes in the same day and sometimes ten years later. There have been several studies in the literature investigating how technical debt in source code accumulates during time and the consequences of this accumulation for software maintenance. However, to the best of our knowledge there are no large scale studies that focus on the types of issues that are fixed and the amount of TD that is paid back during software evolution. In this paper we present the results of a case study, in which we analyzed the evolution of fifty-seven Java open-source software projects by the Apache Software Foundation at the temporal granularity level of weekly snapshots. In particular, we focus on the amount of technical debt that is paid back and the types of issues that are fixed. The findings reveal that a small subset of all issue types is responsible for the largest percentage of TD repayment and thus, targeting particular violations the development team can achieve higher benefits.

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Stability refers to a software system's resistance to the "ripple effect", i.e., propagation of changes. In this paper, we investigate the stability of classes that participate in instances/occurrences of GoF design patterns. We examine whether the stability of such classes is affected by (a) the pattern type, (b) the role that the class plays in the pattern, (c) the number of pattern occurrences in which the class participates, and (d) the application domain. To this end, we conducted a case study on about 65.000 Java open-source classes, where we performed change impact analysis on classes that participate in zero, one (single pattern), or more than one (coupled) pattern occurrences. The results suggest that, the application of design patterns can provide the expected "shielding" of certain pattern-participating classes against changes, depending on their role in the pattern. Moreover, classes that participate in coupled pattern occurrences appear to be the least stable. The results can be used for assessing the benefits and liabilities of the use of patterns and for testing and refactoring prioritization, because less stable classes are expected to require more effort while testing, and urge for refactoring activities that would make them more resistant to change propagation.

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